Assessment of variations in metal concentrations of the Ganges River water by using multivariate statistical techniques

Nazir, A and Khan, MA and Ghosh, P (2022) Assessment of variations in metal concentrations of the Ganges River water by using multivariate statistical techniques. In: Limnologica, 95 .

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Abstract

Worldwide, metal pollution of river waters is a societal problem as human civilization thrives on the bank of rivers, warrants identification of sources and evaluation of possible toxicity to formulate strategies for pollution abatement and sustainable management of water resources. The present study was conducted to document the metal concentrations of surface water samples along the Ganges River using multivariate statistics and to generate information for comparison of water quality. Ba, Cu, Fe, Li, Na and Sr showed significant variations (P < 0.05) both at spatial and temporal scales. Contamination factor and metal index demonstrated that the water in the middle segment stretching from Kanpur to Varanasi is more contaminated and vulnerable to anthropogenic stress. Principal component analysis (PCA) generated four principal components (PCs) with eigenvalues > 1 and these PCs explain the 87.4 of variation in metal concentration. The first two PCs accounted for 52.2 of the total variance and showed a strong correlation with Fe, Li, Mn, Na and Mg. The hierarchical cluster analysis (HCA) shows three clusters based on seasonal sampling at the four locations along the Ganges River. The first two discriminant functions (DFs) explained 99.7 of the variance in metal concentrations among Narora, Kanpur, Varanasi and Bhagalpur sampling locations. Mn, Sr and Na were most significant in the distinction of water samples to their original location with a cross-validation classification accuracy of 63.9. In addition to long-term monitoring programs, the information generated on the variations of metal concentrations can be used to solve the problems of metal pollution of the Ganges River water. © 2022 Elsevier GmbH

Item Type:	Journal Article
Publication:	Limnologica
Publisher:	Elsevier GmbH
Additional Information:	The copyright for this article belongs to the Elsevier GmbH.
Keywords:	Discriminant function analysis; Metal index; Metal pollution; Principal component analysis; Spatial variation; Water quality
Department/Centre:	Division of Mechanical Sciences > Centre for Earth Sciences Division of Interdisciplinary Sciences > Interdisciplinary Centre for Water Research
Date Deposited:	27 Jul 2022 11:33
Last Modified:	27 Jul 2022 11:33
URI:	https://eprints.iisc.ac.in/id/eprint/74997

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